PowerHerd: a distributed scheme for dynamically satisfying peak-power constraints in interconnection networks
Bibliographic record
Abstract
As interconnection networks proliferate to a wide range of high-performance systems, power consumption is becoming a significant architectural issue. In interconnection networks, the peak-power consumption directly affects the solution for package cooling and power-delivery design. Off-line worst-case power analysis is typically used to estimate the network peak-power consumption and guarantee safe online operation, which not only increases system cost, but also constrains network performance. In this paper, we present an online mechanism, called PowerHerd, to efficiently manage network power resources at runtime, and guarantee that network peak-power constraints are not exceeded. PowerHerd is a distributed approach-within the interconnection network, each router dynamically maintains a local power budget, controls its local power dissipation, and exchanges spare power resources with its neighboring routers to optimize network performance. Experiments demonstrate that PowerHerd can effectively regulate network power consumption, meeting peak-power constraints with negligible network-performance penalty. Armed with PowerHerd, network designers can focus on system performance and power optimization for the average case, rather than the worst-case, thus making it possible to employ a more powerful interconnection network in the system.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".